Predicting Cyber Attack Patterns Using Deep Learning Models on Large-Scale Network Traffic Data
Keywords:
deep learning, cyber-attack prediction, network traffic analysis, LSTM, CNN, hybrid models, anomaly detection, cyber threat intelligence, intrusion detection, large-scale dataAbstract
The growing sophistication and frequency of cyber-attacks necessitate advanced detection systems capable of adapting to evolving threats. This study explores the application of deep learning models to predict cyber-attack patterns using large-scale network traffic datasets. Emphasizing temporal and spatial characteristics of attacks, we implement and evaluate multiple architectures including CNNs, LSTMs, and hybrid models on real-world traffic datasets. Our approach focuses on the accurate detection and prediction of malicious behaviors across multiple attack classes such as DDoS, port scanning, and data exfiltration. Results demonstrate the superior performance of deep models over traditional statistical methods, particularly in dynamic traffic scenarios. These findings underscore the viability of deploying end-to-end deep learning frameworks in real-time intrusion detection and threat prediction systems.
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Copyright (c) 2025 Visvanatha Govind Patel (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
